Posts

What the Integration of Cognitive Twin Tech and IoT Will Mean for Consumers and Industry

The integration of cognitive digital twin technology with the Internet of Things (IoT) has the potential to revolutionize the marketplace by providing companies with valuable insights into their products and processes.

What is Cognitive Digital Twin Technology?

Cognitive digital twin technology is a virtual model of a physical system that uses data and artificial intelligence (AI) to simulate and predict the behavior of that system. This technology combines data from sensors and other sources with machine learning algorithms to create a digital representation of a physical system.

A cognitive digital twin model can be used to monitor and analyze the behavior of a system in real-time, and it can be used to simulate the behavior of that system under different conditions. By using this technology, companies can gain insights into the performance of their products, optimize their operations, and reduce maintenance costs.

What is the Internet of Things (IoT)?

The Internet of Things (IoT) is a network of physical devices, vehicles, home appliances, and other items that are embedded with sensors, software, and other technologies that enable them to connect and exchange data with other devices and systems over the Internet.

IoT devices can collect data from their environment, such as temperature, humidity, and pressure, and transmit that data to other devices or systems for analysis. By using IoT devices, companies can monitor their products and processes in real-time and gain insights into how they are performing.

The Impact of Integrating Cognitive Digital Twin Technology With IoT?

Cognitive digital twin technology can be integrated with IoT by using data from IoT devices to create a digital twin model of a physical system. IoT devices can provide data about the performance of a product or process, which can be used to create a digital twin model.

The digital twin model can then be used to simulate the behavior of the physical system under different conditions and to predict how the system will behave in the future. By using IoT data to create a digital twin model, companies can gain insights into the performance of their products and processes, and they can optimize their operations to reduce costs and improve efficiency.

There are several benefits to integrating cognitive digital twin technology with IoT, including:

  1. Predictive Maintenance: By using a cognitive digital twin model, companies can predict when maintenance is required on their products or processes, reducing downtime and maintenance costs.
  2. Improved Efficiency: By monitoring the performance of their products and processes in real-time, companies can optimize their operations to improve efficiency and reduce costs.
  3. Reduced Waste: With CDT, companies can reduce waste by identifying areas where resources are being wasted.
  4. Enhanced Product Design: By using a cognitive digital twin model, companies can simulate the behavior of their products under different conditions and make design changes in the earlier stages of R&D to improve performance, reduce costs, and cut time from POC to market.
  5. Improved Customer Experience: By monitoring the performance of their products in real-time, companies can improve the customer experience by identifying and addressing issues before they become major problems.

How the Market is Already Benefiting from Digital Twin and IoT Technologies

Many industries are already benefiting from the kinds of integration between CDT and IoT technologies. Chief among these is the transportation industry.

Cognitive digital twin technologies coupled with IoT are already proving invaluable for predictive maintenance of high-value military vehicles, airplanes, ships, and even passenger cars. For example, digital twin solutions like those developed by CarTwin extend the lifespan of cars and other vehicles by monitoring the vehicle’s “health” through its “digital twin.”

Basically, CarTwin can provide diagnostic and predictive models for all vehicle systems for which data is available (either directly or indirectly) onboard the vehicle.

Virtually any part of the vehicle that has sensors or that sensors can be developed for can be “twinned.” These data sets are then enhanced and augmented with design and manufacturing data that is already available by the OEM.

Primarily designed for use in fleets of vehicles, in combination with powerful AI models, CarTwin predicts breakdowns, monitors and improves performance, and measures and records real-time greenhouse gas emissions, which reduces expensive maintenance costs and avoids lost revenue associated with fleet downtime.

You can read much more about how AI and digital twin technology in my new book Quantum Care: A Deep Dive into AI for Health Delivery and Research. While the book’s primary focus is on healthcare delivery, it also takes a deep dive into digital twin tech, with an entire chapter devoted to CDT, as well as IoT, and the development and launch of CarTwin!

Rohit Mahajan is a Managing Partner at BigRio and the President and Co-Founder of Citadel Discovery. He has a particular expertise in the development and design of innovative AI and machine learning solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

CarTwin has leveraged AI and Digital Twin technologies to create a digital, cloud-based clone of a physical vehicle designed to detect, prevent, predict, and optimize through AI and real-time analytics. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.

/by

Digital Twin Technology Being Used to Create Next-Gen Aircraft

Digital twin technology is becoming more and more mainstream, particularly when it comes to the maintenance and monitoring of complex mechanical systems such as aircraft.

GENEX, an EU-funded startup, has announced the creation of a new digital twin framework that will be used to create “the next generation” of safer and greener composite aircraft.

Launched on Sept. 1, 2022, GENEX is a 42-month, 5.7 million Horizon Europe collaborative research project seeking to develop an end-to-end digital twin-driven framework for optimized manufacturing and maintenance of next-generation composite aircraft structures.

According to the company, “enhanced computational models will embed interdisciplinary knowledge of aircraft components and manufacturing/repair processes to support their optimization as well as enable data management and monitoring across the entire life cycle of the aircraft’s operation.”

GENEX will really ramp up in the New Year. It is projected to run through 2026.

According to a company press release about the initiative, “The core of the GENEX project is based on three [digital twin] blocks focused on different facets of the aircraft use life, which, eventually, will be integrated into a fourth to form the multidisciplinary digital twin.”

Each block is as follows:

     • Block 1 – Manufacturing Process Digital Twin: An automated tape laying (ATL) process, coupled with hybrid-twin simulation methods, will be developed for the eco-efficient and advance manufacture of recyclable thermoplastic composites.

      • Block 2 – Product Usage Digital Twin: Data- and physics-based machine learning (ML) algorithms for damage detection and location, combined with high-performance computing (HPC)-based multi-physics and AI-powered digital twin tools for fatigue life prediction.

      • Block 3 – MRO Digital Twin: Augmented reality tools, together with novel laser-assisted methods for surface cleaning and monitoring, smart monitoring, and in-situ tailored heating of composite repair blankets, will be further developed to provide additional assistance in manual scarf repair operations, increasing the reliability of the repair process, while supporting the modification and virtual certification of MRO practices.

    • Block 4 – Cognitive Digital Twin: Combined integration of blocks 1-3 for the realization of a digital twin-drive framework implemented into a common industrial internet of things (IIoT) platform.

“The aviation industry is facing a two-fold challenge — targeting carbon neutrality while also adopting the digitalization of next-generation aircraft,” Dr. Calvo-Echenique says. “In GENEX, we hope to provide the needed technological impulse to optimize composite components manufacturing, and operation and repair processes using digital twin strategies.”

Other Industries Benefiting from Digital Twin Technologies

As you can see from the GENEX project, AI and digital twinning are revolutionizing many industries, chief among them transportation. Cognitive digital twin technologies are proving invaluable for the predictive maintenance of high-value military vehicles, airplanes, ships, and even passenger cars. Digital twin solutions like those developed by CarTwin extend the lifespan of cars and other vehicles by monitoring the vehicle’s “health” through its “digital twin.”

Basically, CarTwin can provide diagnostic and predictive models for all vehicle systems for which data is available (either directly or indirectly) onboard the vehicle.
Virtually any part of the vehicle that has sensors or that sensors can be developed for can be “twinned.” These data sets are then enhanced and augmented with design and manufacturing data that is already available by the OEM.

Primarily designed for use in fleets of vehicles, in combination with powerful AI models, CarTwin predicts breakdowns, monitors and improves performance, measures and records real-time greenhouse gas emissions, which reduces expensive maintenance costs and avoids lost revenue associated with fleet downtime.

Rohit Mahajan is a Managing Partner at BigRio and the President and Co-Founder of Citadel Discovery. He has a particular expertise in the development and design of innovative AI and machine learning solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

CarTwin has leveraged AI and Digital Twin technologies to create a digital, cloud-based clone of a physical vehicle designed to detect, prevent, predict, and optimize through AI and real-time analytics. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.

/by

Digital Twins Are Reinventing Clinical Drug Trials

A cognitive digital twin is an AI-driven representation of a real-world system. The objects being twinned can be mechanical such as vehicles, ships, and airplanes, or biological, such as organs and biological processes. It is the latter that is radically altering pharmaceutical research and may very well change the nature of clinical drug trials forever.

Traditional drug discovery is a long and complex process that can take years and millions and millions of dollars. The long and intensive process of bringing a new drug through all phases of clinical trials and to market starts with recruiting the right candidates and then proceeds through many steps and phases of testing the drugs vs. placebos in the candidates that have been recruited for the trial.

Finding those patients is one of the most time-consuming aspects of the process. But that is all changing thanks to AI and, specifically, cognitive digital twin (CDT) technologies.

Cognitive digital twins behave virtually the same way, statistically, as their physical counterparts, which makes them ideal for the powerful ability of AI to assimilate massive amounts of data and make remarkably accurate predictions.

Digital twins have been used quite effectively for monitoring health and providing preventive maintenance for some very highly complex systems, such as high-performance sports cars to military aircraft.

Now, they are changing the very landscape of drug discovery by modeling perhaps the most complex system of all organs and even complete human beings. For example, digital twins of patients are now being used to find ideal candidates in that all-important recruitment phase of a drug trial. The twin is created using AI algorithms and machine learning to create a “virtual patient” by leveraging data from previous clinical trials and from individual patient records. The model predicts how the patient’s health would progress during the course of the trial.

This kind of CDT technology is also being used to create “virtual patients” who are “stand-ins” for the control group – the ones getting a placebo – in the typical double-blind drug trial protocol. The digital twin patient predicts how that individual patient would react if they were given a placebo, essentially creating a simulated control group for a particular patient. Think of it as splitting yourself into two distinct exact copies of yourself, one given the actual drug and the other given the placebo as a control. This makes for an even more accurate control group than just splitting all those in the trial into two groups as in typical trials, because the control group is now exactly the same as the group getting the drug. The digital twin virtually eliminates any variance between the drug group and the placebo group that could be based on genetic, physical, and lifestyle differences between the two groups.

Furthermore, replacing or augmenting control groups with digital twins could help patient volunteers as well as researchers. Most people who join a trial do so, hoping to get a new drug that might help them when already-approved drugs have failed. But there’s a 50/50 chance they’ll be put into the control group and won’t get the experimental treatment. Replacing control groups with digital twins could mean more people have access to experimental drugs.

In Silico Research

And finally, another area where CDT technology is making a tremendous difference in drug discovery is in the emerging area of “in silico” research, where digital twinning is used to create so-called “organs on a chip.” Digital twins of the human heart, lungs, and other organs are already being used to hyper-accelerate drug discovery.

One of the promises of CDT is to make complete in silico drug trials from start to finish a reality. Early successes occurring now are paving the way to a time in the not-so-distant future where no humans, nor animals, not even a single living cell will be required for drug discovery — and yet the impact of any given therapeutic or treatment option on a targeted organ, system or even an individual cell can be perfectly charted.

Citadel and AI for Drug Discovery

AI and machine learning are having a tremendous impact on healthcare in America, from streamlining hospital operations, to improved diagnostics and more intuitive telemedicine applications. However, AI’s greatest impact will likely be in the way digital twins and other AI solutions are revolutionizing pharmaceutical research.

To that end, Citadel Discovery was launched in 2021 with the purpose of giving a kind of “open access” to the data and technology that will drive the future of pharma research streamlining and lowering the costs of drug discovery and biological research.

The costs of drug discovery continue to rise, with current estimates exceeding $2 Billion. Not to mention that bringing a drug successfully through all clinical trial phases takes, on average, 10-12 years in research and development. Artificial intelligence and machine learning in drug discovery hold the key to reducing these costs and timelines.

Rohit Mahajan is the President and Co-Founder of Citadel Discovery. He has a particular expertise in the development and design of innovative solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

Citadel Discovery is dedicated to leveraging AI and MI for the purpose of democratizing access to the data and technology that will drive the future of biological exploration, drug discovery, and health technologies. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.

/by